陳良富 王力 李昂 柴子為 王子峰

摘 要：為尋求一系列能敏感、清晰地反映生態(tài)系統(tǒng)站木特征及生態(tài)環(huán)境變化趨勢(shì)的定量遙感參數(shù)，構(gòu)建可業(yè)務(wù)化的定量生態(tài)環(huán)境質(zhì)量監(jiān)測(cè)指標(biāo)體系，以在時(shí)究尺度上對(duì)特定區(qū)域范圍內(nèi)生態(tài)系統(tǒng)和生態(tài)系統(tǒng)組合體的類型、結(jié)構(gòu)和功能及其組合要素進(jìn)行系統(tǒng)地測(cè)定，研究圍繞生態(tài)系統(tǒng)和景觀生態(tài)學(xué)等理念，從區(qū)域植被組成狀況、植被結(jié)構(gòu)狀況和植被功能狀況3個(gè)方面入手。在多源、多尺度立體監(jiān)測(cè)數(shù)據(jù)生態(tài)環(huán)境參數(shù)提取研究基礎(chǔ)上，重點(diǎn)研究與生產(chǎn)生態(tài)環(huán)境質(zhì)量監(jiān)測(cè)與評(píng)價(jià)的相關(guān)參數(shù)產(chǎn)品。2014年的主要研究工作為，在基于物候信息和數(shù)據(jù)擬合算法的支持下，進(jìn)行了多源遙感數(shù)據(jù)農(nóng)作物精細(xì)分類、植被遙感決策樹分類等方法模型研究；根據(jù)NDVI隨觀測(cè)角度的變化規(guī)律構(gòu)造出新型多角度歸一化植被指數(shù)MNDVI，用以解決LAI反演中過(guò)飽和問(wèn)題，并將實(shí)測(cè)和估算LAI進(jìn)行線性回歸來(lái)進(jìn)行精度驗(yàn)證。嘗試通過(guò)結(jié)構(gòu)方程模型建立地表生物量（或LAI）及土壤有機(jī)碳與土壤呼吸的關(guān)系，實(shí)現(xiàn)空間上大面積的土壤有機(jī)碳含量估算，并進(jìn)行土壤呼吸情況的空間分布估計(jì)。衛(wèi)星遙感已被廣泛用于大氣環(huán)境監(jiān)測(cè)與氣候變化研究之中，其大范圍、動(dòng)態(tài)連續(xù)的觀測(cè)能力對(duì)地面觀測(cè)形成了有益的補(bǔ)充。該研究基于我國(guó)大氣環(huán)境監(jiān)測(cè)的實(shí)際需要，基于衛(wèi)星觀測(cè)與大氣模式、氣象數(shù)據(jù)相結(jié)合的思路，初步實(shí)現(xiàn)了對(duì)流層HCHO柱濃度反演、近地面N02濃度估算，以及基于常規(guī)期限觀測(cè)的氣溶膠吸濕增長(zhǎng)特性近似等方法的研究，為進(jìn)一步滿足實(shí)際的業(yè)務(wù)監(jiān)測(cè)需要，提高遙感反演精度提供支撐。地基大氣痕量氣體與氣溶膠遙感儀器主要用于獲取大氣痕量氣體柱濃度和垂直分布廓線以及氣溶膠光學(xué)厚度和消光系數(shù)廓線，為星載、機(jī)載數(shù)據(jù)驗(yàn)證提供技術(shù)手段。該年度完成了地基大氣痕量氣體與氣溶膠紫外可見光譜遙感探測(cè)系統(tǒng)的總體設(shè)計(jì)、各項(xiàng)關(guān)鍵分系統(tǒng)的設(shè)計(jì)和加工，包括二維掃描和望遠(yuǎn)鏡單元、光譜探測(cè)單元、恒溫控制單元等，開展了紫外可見多組分氣體濃度反演算法研究等。并進(jìn)行了實(shí)驗(yàn)室測(cè)試及實(shí)驗(yàn)工作，測(cè)試了系統(tǒng)關(guān)鍵部件和驗(yàn)收數(shù)據(jù)處理算法。

關(guān)鍵詞：中文生態(tài)環(huán)境遙感、大氣環(huán)境遙感、地基大氣遙感、示范應(yīng)用、系統(tǒng)開發(fā)集成

Abstract：This study started from regional vegetation composition status， vegetation structural status and vegetation functional status surrounding ecosystem and landscape ecology concepts. Based on the study of ecological parameter extraction from multi-source， multi-scale and multi-dimension monitoring data， this study focus on parameter productions concerning ecological environment quality monitoring and evaluation. The main work in the year 2014 was， the study of multi-source remote sensing data fine crop classification， decision tree classification of remote sensing of vegetation and other methods and models with the support of phenological information and data fitting algorithm. According to the observed NDVI variation with angle， constructed a new multi-angle normalized difference vegetation index MNDVI， to solve the problem of LAI oversaturated retrieval， and make accuracy verification of measured and estimated LAI. Attempt to establish the relationship between above ground biomass （or LAI） and soil organic carbon by building structural equation model， to estimate soil organic carbon content in a large spatial area， and to estimate spatial distribution of soil respiration. Satellite remote sensing has been widely used in atmospheric environment monitoring and climate change researches. This study developed initial method of estimating ground-level NO2 using satellite observations and atmospheric model simulations. Also， a new approach of approximating hygroscopic growth behaviors of particles based on meteorological measurements， such as VIS and RH， has been established and validated initially.Ground-based trace gases and aerosol telemetry instrument is used to achieve the column density of trace gases and vertical profile， along with the aerosol optical density and extinction coefficient profile. The main progress of this year is mainly focused on the overall design of the ground-based trace gases and aerosol telemetry instrument applied in ultraviolet visible wavelength； design and manufacture of the key subsystem， including two-dimensional scanning telescope， spectrum detection unit， constant temperature control unit； and research of UV visible multi component gas concentration retrieval algorithm. Laboratory test and the experimental work have been carried out， the key parts of the system and data processing algorithm has been tested.

Key Words：Ecological Environment Remote Sensing；Atmospheric Environment Remote Sensing；Ground-based Atmospheric Remote Sensing， Demonstrative Application；System Development and Integration

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